Blog Archives

By Susan Grajek, Vice President for data, research and analytics, EDUCAUSE.

Big data and analytics are reshaping everything. Industry is using them to great effect, to better understand markets and customers, manage supply chains, and increase profits. Personalized medicine, fueled by analytics applied to big data, is poised to revolutionize healthcare. Higher education lags several paces behind these fields. Some institutions are demonstrating improvements in retention and degree completion, but most are still using data to monitor student outcomes and activities rather than predict or proactively intervene.

Certainly, trends related to analytics and data are influencing institutional IT strategy, more so than other types of trends EDUCAUSE tracks, including those related to teaching and learning and security and risk1. Data-driven decision-making, enterprise data management, and data integration issues are all already incorporated into or exerting a major influence on emerging IT strategy in at least half of US colleges and universities. Personalized learning, however, is only this influential at one in five institutions.

By Sara M. Watson, technology critic and fellow at the Berkman Center for Internet and Society.

What would it take to map the Internet? Not just the links, connecting the web of sites to each other, or some map of the network of networks. That’s hard enough in itself.

What if we were to map the flows of data around the Internet? Not just delivering packets, but what those packets contain, where they propagate, how they are passed on, and to what ends they are used.

Between our browser history, cookies, social platforms, sensors, brokers, and beyond, there are myriad parties with economic interests in our data. How those parties interconnect and trade in our data is, for the most part, opaque to us.

Every day we generate a huge amount of big data, but we need to resort to analytics to make abstract information meaningful and get valuable knowledge from it. In education, learning platforms let us easily gather an immense quantity of data regarding students’ behaviour, interactions, preferences and opinions. When properly analysed — through learning analytics — all these data might provide useful insight on how to make learning processes more adaptive, attractive and efficient.

Are these techniques allowing us to provide better support to our students? Are we taking advantage of big data and analytics to help shape the citizens of the future?